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Neuro-fuzzy and genetic algorithm in multiple response optimization

✍ Scribed by Chi-Bin Cheng; C.-J. Cheng; E.S. Lee

Publisher: Elsevier Science
Year: 2002
Tongue: English
Weight: 825 KB
Volume: 44
Category: Article
ISSN: 0898-1221
DOI: 10.1016/s0898-1221(02)00274-2

No coin nor oath required. For personal study only.

✦ Synopsis

Optimization of a multiple output system, whose function is only approximately known and is represented in tabular form, is modeled and optimized by the combined use of a neuro-fuzzy network and optimization techniques which do not require the explicit representation of the function. Neuro-fuzzy network is useful for learning the approximate original tabular system. However, the results obtained by the neuro-fuzzy network are represented implicitly in the network. The MANFIS neuro-fuzzy network, which is an extension of the ANFIS network, is used to model the multiple output system and a genetic algorithm is used to optimize the resulting multiple objective decision making problem. A chemical process whose function is represented approximately in tabular form is solved to illustrate the approach.

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